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/*
* Copyright (c) 2016-2020 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef __UTILS_UTILS_H__
#define __UTILS_UTILS_H__
/** @dir .
* brief Boiler plate code used by examples. Various utilities to print types, load / store assets, etc.
*/
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Window.h"
#include "arm_compute/runtime/Tensor.h"
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wunused-parameter"
#pragma GCC diagnostic ignored "-Wstrict-overflow"
#include "libnpy/npy.hpp"
#pragma GCC diagnostic pop
#include "support/MemorySupport.h"
#include "support/StringSupport.h"
#ifdef ARM_COMPUTE_CL
#include "arm_compute/core/CL/OpenCL.h"
#include "arm_compute/runtime/CL/CLDistribution1D.h"
#include "arm_compute/runtime/CL/CLTensor.h"
#endif /* ARM_COMPUTE_CL */
#ifdef ARM_COMPUTE_GC
#include "arm_compute/runtime/GLES_COMPUTE/GCTensor.h"
#endif /* ARM_COMPUTE_GC */
#include <cstdlib>
#include <cstring>
#include <fstream>
#include <iostream>
#include <random>
#include <string>
#include <tuple>
#include <vector>
namespace arm_compute
{
namespace utils
{
/** Supported image types */
enum class ImageType
{
UNKNOWN,
PPM,
JPEG
};
/** Abstract Example class.
*
* All examples have to inherit from this class.
*/
class Example
{
public:
/** Setup the example.
*
* @param[in] argc Argument count.
* @param[in] argv Argument values.
*
* @return True in case of no errors in setup else false
*/
virtual bool do_setup(int argc, char **argv)
{
ARM_COMPUTE_UNUSED(argc, argv);
return true;
};
/** Run the example. */
virtual void do_run() {};
/** Teardown the example. */
virtual void do_teardown() {};
/** Default destructor. */
virtual ~Example() = default;
};
/** Run an example and handle the potential exceptions it throws
*
* @param[in] argc Number of command line arguments
* @param[in] argv Command line arguments
* @param[in] example Example to run
*/
int run_example(int argc, char **argv, std::unique_ptr<Example> example);
template <typename T>
int run_example(int argc, char **argv)
{
return run_example(argc, argv, support::cpp14::make_unique<T>());
}
/** Draw a RGB rectangular window for the detected object
*
* @param[in, out] tensor Input tensor where the rectangle will be drawn on. Format supported: RGB888
* @param[in] rect Geometry of the rectangular window
* @param[in] r Red colour to use
* @param[in] g Green colour to use
* @param[in] b Blue colour to use
*/
void draw_detection_rectangle(arm_compute::ITensor *tensor, const arm_compute::DetectionWindow &rect, uint8_t r, uint8_t g, uint8_t b);
/** Gets image type given a file
*
* @param[in] filename File to identify its image type
*
* @return Image type
*/
ImageType get_image_type_from_file(const std::string &filename);
/** Parse the ppm header from an input file stream. At the end of the execution,
* the file position pointer will be located at the first pixel stored in the ppm file
*
* @param[in] fs Input file stream to parse
*
* @return The width, height and max value stored in the header of the PPM file
*/
std::tuple<unsigned int, unsigned int, int> parse_ppm_header(std::ifstream &fs);
/** Parse the npy header from an input file stream. At the end of the execution,
* the file position pointer will be located at the first pixel stored in the npy file //TODO
*
* @param[in] fs Input file stream to parse
*
* @return The width and height stored in the header of the NPY file
*/
std::tuple<std::vector<unsigned long>, bool, std::string> parse_npy_header(std::ifstream &fs);
/** Obtain numpy type string from DataType.
*
* @param[in] data_type Data type.
*
* @return numpy type string.
*/
inline std::string get_typestring(DataType data_type)
{
// Check endianness
const unsigned int i = 1;
const char *c = reinterpret_cast<const char *>(&i);
std::string endianness;
if(*c == 1)
{
endianness = std::string("<");
}
else
{
endianness = std::string(">");
}
const std::string no_endianness("|");
switch(data_type)
{
case DataType::U8:
case DataType::QASYMM8:
return no_endianness + "u" + support::cpp11::to_string(sizeof(uint8_t));
case DataType::S8:
case DataType::QSYMM8:
case DataType::QSYMM8_PER_CHANNEL:
return no_endianness + "i" + support::cpp11::to_string(sizeof(int8_t));
case DataType::U16:
case DataType::QASYMM16:
return endianness + "u" + support::cpp11::to_string(sizeof(uint16_t));
case DataType::S16:
case DataType::QSYMM16:
return endianness + "i" + support::cpp11::to_string(sizeof(int16_t));
case DataType::U32:
return endianness + "u" + support::cpp11::to_string(sizeof(uint32_t));
case DataType::S32:
return endianness + "i" + support::cpp11::to_string(sizeof(int32_t));
case DataType::U64:
return endianness + "u" + support::cpp11::to_string(sizeof(uint64_t));
case DataType::S64:
return endianness + "i" + support::cpp11::to_string(sizeof(int64_t));
case DataType::F16:
return endianness + "f" + support::cpp11::to_string(sizeof(half));
case DataType::F32:
return endianness + "f" + support::cpp11::to_string(sizeof(float));
case DataType::F64:
return endianness + "f" + support::cpp11::to_string(sizeof(double));
case DataType::SIZET:
return endianness + "u" + support::cpp11::to_string(sizeof(size_t));
default:
ARM_COMPUTE_ERROR("Data type not supported");
}
}
/** Maps a tensor if needed
*
* @param[in] tensor Tensor to be mapped
* @param[in] blocking Specified if map is blocking or not
*/
template <typename T>
inline void map(T &tensor, bool blocking)
{
ARM_COMPUTE_UNUSED(tensor);
ARM_COMPUTE_UNUSED(blocking);
}
/** Unmaps a tensor if needed
*
* @param tensor Tensor to be unmapped
*/
template <typename T>
inline void unmap(T &tensor)
{
ARM_COMPUTE_UNUSED(tensor);
}
#ifdef ARM_COMPUTE_CL
/** Maps a tensor if needed
*
* @param[in] tensor Tensor to be mapped
* @param[in] blocking Specified if map is blocking or not
*/
inline void map(CLTensor &tensor, bool blocking)
{
tensor.map(blocking);
}
/** Unmaps a tensor if needed
*
* @param tensor Tensor to be unmapped
*/
inline void unmap(CLTensor &tensor)
{
tensor.unmap();
}
/** Maps a distribution if needed
*
* @param[in] distribution Distribution to be mapped
* @param[in] blocking Specified if map is blocking or not
*/
inline void map(CLDistribution1D &distribution, bool blocking)
{
distribution.map(blocking);
}
/** Unmaps a distribution if needed
*
* @param distribution Distribution to be unmapped
*/
inline void unmap(CLDistribution1D &distribution)
{
distribution.unmap();
}
#endif /* ARM_COMPUTE_CL */
#ifdef ARM_COMPUTE_GC
/** Maps a tensor if needed
*
* @param[in] tensor Tensor to be mapped
* @param[in] blocking Specified if map is blocking or not
*/
inline void map(GCTensor &tensor, bool blocking)
{
tensor.map(blocking);
}
/** Unmaps a tensor if needed
*
* @param tensor Tensor to be unmapped
*/
inline void unmap(GCTensor &tensor)
{
tensor.unmap();
}
#endif /* ARM_COMPUTE_GC */
/** Specialized class to generate random non-zero FP16 values.
* uniform_real_distribution<half> generates values that get rounded off to zero, causing
* differences between ACL and reference implementation
*/
class uniform_real_distribution_fp16
{
half min{ 0.0f }, max{ 0.0f };
std::uniform_real_distribution<float> neg{ min, -0.3f };
std::uniform_real_distribution<float> pos{ 0.3f, max };
std::uniform_int_distribution<uint8_t> sign_picker{ 0, 1 };
public:
using result_type = half;
/** Constructor
*
* @param[in] a Minimum value of the distribution
* @param[in] b Maximum value of the distribution
*/
explicit uniform_real_distribution_fp16(half a = half(0.0), half b = half(1.0))
: min(a), max(b)
{
}
/** () operator to generate next value
*
* @param[in] gen an uniform random bit generator object
*/
half operator()(std::mt19937 &gen)
{
if(sign_picker(gen))
{
return (half)neg(gen);
}
return (half)pos(gen);
}
};
/** Numpy data loader */
class NPYLoader
{
public:
/** Default constructor */
NPYLoader()
: _fs(), _shape(), _fortran_order(false), _typestring(), _file_layout(DataLayout::NCHW)
{
}
/** Open a NPY file and reads its metadata
*
* @param[in] npy_filename File to open
* @param[in] file_layout (Optional) Layout in which the weights are stored in the file.
*/
void open(const std::string &npy_filename, DataLayout file_layout = DataLayout::NCHW)
{
ARM_COMPUTE_ERROR_ON(is_open());
try
{
_fs.open(npy_filename, std::ios::in | std::ios::binary);
ARM_COMPUTE_EXIT_ON_MSG_VAR(!_fs.good(), "Failed to load binary data from %s", npy_filename.c_str());
_fs.exceptions(std::ifstream::failbit | std::ifstream::badbit);
_file_layout = file_layout;
std::tie(_shape, _fortran_order, _typestring) = parse_npy_header(_fs);
}
catch(const std::ifstream::failure &e)
{
ARM_COMPUTE_ERROR_VAR("Accessing %s: %s", npy_filename.c_str(), e.what());
}
}
/** Return true if a NPY file is currently open */
bool is_open()
{
return _fs.is_open();
}
/** Return true if a NPY file is in fortran order */
bool is_fortran()
{
return _fortran_order;
}
/** Initialise the tensor's metadata with the dimensions of the NPY file currently open
*
* @param[out] tensor Tensor to initialise
* @param[in] dt Data type to use for the tensor
*/
template <typename T>
void init_tensor(T &tensor, arm_compute::DataType dt)
{
ARM_COMPUTE_ERROR_ON(!is_open());
ARM_COMPUTE_ERROR_ON(dt != arm_compute::DataType::F32);
// Use the size of the input NPY tensor
TensorShape shape;
shape.set_num_dimensions(_shape.size());
for(size_t i = 0; i < _shape.size(); ++i)
{
size_t src = i;
if(_fortran_order)
{
src = _shape.size() - 1 - i;
}
shape.set(i, _shape.at(src));
}
arm_compute::TensorInfo tensor_info(shape, 1, dt);
tensor.allocator()->init(tensor_info);
}
/** Fill a tensor with the content of the currently open NPY file.
*
* @note If the tensor is a CLTensor, the function maps and unmaps the tensor
*
* @param[in,out] tensor Tensor to fill (Must be allocated, and of matching dimensions with the opened NPY).
*/
template <typename T>
void fill_tensor(T &tensor)
{
ARM_COMPUTE_ERROR_ON(!is_open());
ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(&tensor, arm_compute::DataType::QASYMM8, arm_compute::DataType::S32, arm_compute::DataType::F32, arm_compute::DataType::F16);
try
{
// Map buffer if creating a CLTensor
map(tensor, true);
// Check if the file is large enough to fill the tensor
const size_t current_position = _fs.tellg();
_fs.seekg(0, std::ios_base::end);
const size_t end_position = _fs.tellg();
_fs.seekg(current_position, std::ios_base::beg);
ARM_COMPUTE_ERROR_ON_MSG((end_position - current_position) < tensor.info()->tensor_shape().total_size() * tensor.info()->element_size(),
"Not enough data in file");
ARM_COMPUTE_UNUSED(end_position);
// Check if the typestring matches the given one
std::string expect_typestr = get_typestring(tensor.info()->data_type());
ARM_COMPUTE_ERROR_ON_MSG(_typestring != expect_typestr, "Typestrings mismatch");
bool are_layouts_different = (_file_layout != tensor.info()->data_layout());
// Correct dimensions (Needs to match TensorShape dimension corrections)
if(_shape.size() != tensor.info()->tensor_shape().num_dimensions())
{
for(int i = static_cast<int>(_shape.size()) - 1; i > 0; --i)
{
if(_shape[i] == 1)
{
_shape.pop_back();
}
else
{
break;
}
}
}
TensorShape permuted_shape = tensor.info()->tensor_shape();
arm_compute::PermutationVector perm;
if(are_layouts_different && tensor.info()->tensor_shape().num_dimensions() > 2)
{
perm = (tensor.info()->data_layout() == arm_compute::DataLayout::NHWC) ? arm_compute::PermutationVector(2U, 0U, 1U) : arm_compute::PermutationVector(1U, 2U, 0U);
arm_compute::PermutationVector perm_vec = (tensor.info()->data_layout() == arm_compute::DataLayout::NCHW) ? arm_compute::PermutationVector(2U, 0U, 1U) : arm_compute::PermutationVector(1U, 2U, 0U);
arm_compute::permute(permuted_shape, perm_vec);
}
// Validate tensor shape
ARM_COMPUTE_ERROR_ON_MSG(_shape.size() != tensor.info()->tensor_shape().num_dimensions(), "Tensor ranks mismatch");
for(size_t i = 0; i < _shape.size(); ++i)
{
ARM_COMPUTE_ERROR_ON_MSG(permuted_shape[i] != _shape[i], "Tensor dimensions mismatch");
}
switch(tensor.info()->data_type())
{
case arm_compute::DataType::QASYMM8:
case arm_compute::DataType::S32:
case arm_compute::DataType::F32:
case arm_compute::DataType::F16:
{
// Read data
if(!are_layouts_different && !_fortran_order && tensor.info()->padding().empty())
{
// If tensor has no padding read directly from stream.
_fs.read(reinterpret_cast<char *>(tensor.buffer()), tensor.info()->total_size());
}
else
{
// If tensor has padding or is in fortran order accessing tensor elements through execution window.
Window window;
const unsigned int num_dims = _shape.size();
if(_fortran_order)
{
for(unsigned int dim = 0; dim < num_dims; dim++)
{
permuted_shape.set(dim, _shape[num_dims - dim - 1]);
perm.set(dim, num_dims - dim - 1);
}
if(are_layouts_different)
{
// Permute only if num_dimensions greater than 2
if(num_dims > 2)
{
if(_file_layout == DataLayout::NHWC) // i.e destination is NCHW --> permute(1,2,0)
{
arm_compute::permute(perm, arm_compute::PermutationVector(1U, 2U, 0U));
}
else
{
arm_compute::permute(perm, arm_compute::PermutationVector(2U, 0U, 1U));
}
}
}
}
window.use_tensor_dimensions(permuted_shape);
execute_window_loop(window, [&](const Coordinates & id)
{
Coordinates dst(id);
arm_compute::permute(dst, perm);
_fs.read(reinterpret_cast<char *>(tensor.ptr_to_element(dst)), tensor.info()->element_size());
});
}
break;
}
default:
ARM_COMPUTE_ERROR("Unsupported data type");
}
// Unmap buffer if creating a CLTensor
unmap(tensor);
}
catch(const std::ifstream::failure &e)
{
ARM_COMPUTE_ERROR_VAR("Loading NPY file: %s", e.what());
}
}
private:
std::ifstream _fs;
std::vector<unsigned long> _shape;
bool _fortran_order;
std::string _typestring;
DataLayout _file_layout;
};
/** Template helper function to save a tensor image to a PPM file.
*
* @note Only U8 and RGB888 formats supported.
* @note Only works with 2D tensors.
* @note If the input tensor is a CLTensor, the function maps and unmaps the image
*
* @param[in] tensor The tensor to save as PPM file
* @param[in] ppm_filename Filename of the file to create.
*/
template <typename T>
void save_to_ppm(T &tensor, const std::string &ppm_filename)
{
ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(&tensor, arm_compute::Format::RGB888, arm_compute::Format::U8);
ARM_COMPUTE_ERROR_ON(tensor.info()->num_dimensions() > 2);
std::ofstream fs;
try
{
fs.exceptions(std::ofstream::failbit | std::ofstream::badbit | std::ofstream::eofbit);
fs.open(ppm_filename, std::ios::out | std::ios::binary);
const unsigned int width = tensor.info()->tensor_shape()[0];
const unsigned int height = tensor.info()->tensor_shape()[1];
fs << "P6\n"
<< width << " " << height << " 255\n";
// Map buffer if creating a CLTensor/GCTensor
map(tensor, true);
switch(tensor.info()->format())
{
case arm_compute::Format::U8:
{
arm_compute::Window window;
window.set(arm_compute::Window::DimX, arm_compute::Window::Dimension(0, width, 1));
window.set(arm_compute::Window::DimY, arm_compute::Window::Dimension(0, height, 1));
arm_compute::Iterator in(&tensor, window);
arm_compute::execute_window_loop(window, [&](const arm_compute::Coordinates &)
{
const unsigned char value = *in.ptr();
fs << value << value << value;
},
in);
break;
}
case arm_compute::Format::RGB888:
{
arm_compute::Window window;
window.set(arm_compute::Window::DimX, arm_compute::Window::Dimension(0, width, width));
window.set(arm_compute::Window::DimY, arm_compute::Window::Dimension(0, height, 1));
arm_compute::Iterator in(&tensor, window);
arm_compute::execute_window_loop(window, [&](const arm_compute::Coordinates &)
{
fs.write(reinterpret_cast<std::fstream::char_type *>(in.ptr()), width * tensor.info()->element_size());
},
in);
break;
}
default:
ARM_COMPUTE_ERROR("Unsupported format");
}
// Unmap buffer if creating a CLTensor/GCTensor
unmap(tensor);
}
catch(const std::ofstream::failure &e)
{
ARM_COMPUTE_ERROR_VAR("Writing %s: (%s)", ppm_filename.c_str(), e.what());
}
}
/** Template helper function to save a tensor image to a NPY file.
*
* @note Only F32 data type supported.
* @note If the input tensor is a CLTensor, the function maps and unmaps the image
*
* @param[in] tensor The tensor to save as NPY file
* @param[in] npy_filename Filename of the file to create.
* @param[in] fortran_order If true, save matrix in fortran order.
*/
template <typename T, typename U = float>
void save_to_npy(T &tensor, const std::string &npy_filename, bool fortran_order)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(&tensor, arm_compute::DataType::F32, arm_compute::DataType::QASYMM8);
std::ofstream fs;
try
{
fs.exceptions(std::ofstream::failbit | std::ofstream::badbit | std::ofstream::eofbit);
fs.open(npy_filename, std::ios::out | std::ios::binary);
std::vector<npy::ndarray_len_t> shape(tensor.info()->num_dimensions());
for(unsigned int i = 0, j = tensor.info()->num_dimensions() - 1; i < tensor.info()->num_dimensions(); ++i, --j)
{
shape[i] = tensor.info()->tensor_shape()[!fortran_order ? j : i];
}
// Map buffer if creating a CLTensor
map(tensor, true);
using typestring_type = typename std::conditional<std::is_floating_point<U>::value, float, qasymm8_t>::type;
std::vector<typestring_type> tmp; /* Used only to get the typestring */
npy::Typestring typestring_o{ tmp };
std::string typestring = typestring_o.str();
std::ofstream stream(npy_filename, std::ofstream::binary);
npy::write_header(stream, typestring, fortran_order, shape);
arm_compute::Window window;
window.use_tensor_dimensions(tensor.info()->tensor_shape());
arm_compute::Iterator in(&tensor, window);
arm_compute::execute_window_loop(window, [&](const arm_compute::Coordinates &)
{
stream.write(reinterpret_cast<const char *>(in.ptr()), sizeof(typestring_type));
},
in);
// Unmap buffer if creating a CLTensor
unmap(tensor);
}
catch(const std::ofstream::failure &e)
{
ARM_COMPUTE_ERROR_VAR("Writing %s: (%s)", npy_filename.c_str(), e.what());
}
}
/** Load the tensor with pre-trained data from a binary file
*
* @param[in] tensor The tensor to be filled. Data type supported: F32.
* @param[in] filename Filename of the binary file to load from.
*/
template <typename T>
void load_trained_data(T &tensor, const std::string &filename)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32);
std::ifstream fs;
try
{
fs.exceptions(std::ofstream::failbit | std::ofstream::badbit | std::ofstream::eofbit);
// Open file
fs.open(filename, std::ios::in | std::ios::binary);
if(!fs.good())
{
throw std::runtime_error("Could not load binary data: " + filename);
}
// Map buffer if creating a CLTensor/GCTensor
map(tensor, true);
Window window;
window.set(arm_compute::Window::DimX, arm_compute::Window::Dimension(0, 1, 1));
for(unsigned int d = 1; d < tensor.info()->num_dimensions(); ++d)
{
window.set(d, Window::Dimension(0, tensor.info()->tensor_shape()[d], 1));
}
arm_compute::Iterator in(&tensor, window);
execute_window_loop(window, [&](const Coordinates &)
{
fs.read(reinterpret_cast<std::fstream::char_type *>(in.ptr()), tensor.info()->tensor_shape()[0] * tensor.info()->element_size());
},
in);
// Unmap buffer if creating a CLTensor/GCTensor
unmap(tensor);
}
catch(const std::ofstream::failure &e)
{
ARM_COMPUTE_ERROR_VAR("Writing %s: (%s)", filename.c_str(), e.what());
}
}
template <typename T>
void fill_random_tensor(T &tensor, float lower_bound, float upper_bound)
{
std::random_device rd;
std::mt19937 gen(rd());
Window window;
window.use_tensor_dimensions(tensor.info()->tensor_shape());
map(tensor, true);
Iterator it(&tensor, window);
switch(tensor.info()->data_type())
{
case arm_compute::DataType::F16:
{
std::uniform_real_distribution<float> dist(lower_bound, upper_bound);
execute_window_loop(window, [&](const Coordinates &)
{
*reinterpret_cast<half *>(it.ptr()) = (half)dist(gen);
},
it);
break;
}
case arm_compute::DataType::F32:
{
std::uniform_real_distribution<float> dist(lower_bound, upper_bound);
execute_window_loop(window, [&](const Coordinates &)
{
*reinterpret_cast<float *>(it.ptr()) = dist(gen);
},
it);
break;
}
default:
{
ARM_COMPUTE_ERROR("Unsupported format");
}
}
unmap(tensor);
}
template <typename T>
void init_sgemm_output(T &dst, T &src0, T &src1, arm_compute::DataType dt)
{
dst.allocator()->init(TensorInfo(TensorShape(src1.info()->dimension(0), src0.info()->dimension(1), src0.info()->dimension(2)), 1, dt));
}
/** This function returns the amount of memory free reading from /proc/meminfo
*
* @return The free memory in kB
*/
uint64_t get_mem_free_from_meminfo();
/** Compare two tensors
*
* @param[in] tensor1 First tensor to be compared.
* @param[in] tensor2 Second tensor to be compared.
* @param[in] tolerance Tolerance used for the comparison.
*
* @return The number of mismatches
*/
template <typename T>
int compare_tensor(ITensor &tensor1, ITensor &tensor2, T tolerance)
{
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(&tensor1, &tensor2);
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(&tensor1, &tensor2);
int num_mismatches = 0;
Window window;
window.use_tensor_dimensions(tensor1.info()->tensor_shape());
map(tensor1, true);
map(tensor2, true);
Iterator itensor1(&tensor1, window);
Iterator itensor2(&tensor2, window);
execute_window_loop(window, [&](const Coordinates &)
{
if(std::abs(*reinterpret_cast<T *>(itensor1.ptr()) - *reinterpret_cast<T *>(itensor2.ptr())) > tolerance)
{
++num_mismatches;
}
},
itensor1, itensor2);
unmap(itensor1);
unmap(itensor2);
return num_mismatches;
}
} // namespace utils
} // namespace arm_compute
#endif /* __UTILS_UTILS_H__*/
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